Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "51" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.247230 | 1.881748 | -0.325029 | 0.175193 | 2.365456 | 2.914956 | 65.722287 | 1.784760 | 0.5810 | 0.5872 | 0.3743 | nan | nan |
| 2460009 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 4.588700 | 2.030976 | -0.346204 | 0.188049 | 0.979248 | 1.364839 | 70.992282 | 1.908632 | 0.5878 | 0.5984 | 0.3764 | nan | nan |
| 2460008 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.750122 | 2.017569 | -0.023234 | -0.091244 | 1.602641 | 1.495115 | 15.628826 | 1.101181 | 0.6271 | 0.6414 | 0.3376 | nan | nan |
| 2460007 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.016692 | 1.758469 | 0.151539 | 0.159264 | 1.822893 | 1.940729 | 28.512872 | 0.176687 | 0.5805 | 0.6035 | 0.3630 | nan | nan |
| 2459999 | dish_maintenance | 0.00% | 99.92% | 100.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.1606 | 0.1043 | 0.0544 | nan | nan |
| 2459998 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 5.431539 | 1.598764 | 0.123779 | 0.183021 | 3.761807 | 2.073243 | 34.215793 | 0.650724 | 0.5887 | 0.6058 | 0.3943 | nan | nan |
| 2459997 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 6.446513 | 1.687970 | 0.083252 | 0.268664 | 2.458136 | 0.988358 | 51.103773 | 0.443684 | 0.6024 | 0.6228 | 0.3943 | nan | nan |
| 2459996 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.047061 | 2.092761 | 0.214648 | 0.139180 | 3.354883 | 1.035974 | 26.671990 | 2.095241 | 0.6090 | 0.6269 | 0.4054 | nan | nan |
| 2459995 | dish_maintenance | 100.00% | 100.00% | 100.00% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| 2459994 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.936275 | 4.519721 | -0.490373 | 1.334129 | 3.503706 | 3.801128 | 22.730507 | 2.077481 | 0.4959 | 0.5028 | 0.2561 | nan | nan |
| 2459993 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.959423 | 4.608005 | 0.038723 | 1.865038 | 4.314456 | 4.049612 | 19.337135 | 2.907614 | 0.4293 | 0.4635 | 0.2575 | nan | nan |
| 2459991 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.569575 | 5.223066 | 0.189829 | 1.942266 | 3.631252 | 4.405093 | 25.743274 | 3.772217 | 0.5064 | 0.5080 | 0.2604 | nan | nan |
| 2459990 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.115566 | 4.269160 | 0.242811 | 1.974764 | 4.436532 | 3.896251 | 37.729546 | 3.611318 | 0.5021 | 0.5059 | 0.2579 | nan | nan |
| 2459989 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.659005 | 4.201771 | 0.177629 | 2.026232 | 4.606690 | 3.297131 | 33.154457 | 3.706910 | 0.4983 | 0.5060 | 0.2578 | nan | nan |
| 2459988 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.904476 | 5.199686 | 0.165336 | 1.883782 | 4.761895 | 3.987447 | 30.269848 | 1.482734 | 0.4978 | 0.5104 | 0.2522 | nan | nan |
| 2459987 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.860834 | 4.160895 | 0.142020 | 1.873290 | 3.748032 | 3.355355 | 44.607472 | 0.743372 | 0.5023 | 0.5113 | 0.2511 | nan | nan |
| 2459986 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.450322 | 5.116341 | 0.201874 | 1.923145 | 5.689827 | 4.621762 | 22.913180 | 1.507016 | 0.5292 | 0.5447 | 0.2277 | nan | nan |
| 2459985 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.665547 | 5.017981 | 0.091672 | 1.809779 | 3.906339 | 3.564587 | 57.248630 | 5.503310 | 0.4971 | 0.5080 | 0.2561 | nan | nan |
| 2459984 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 12.434703 | 4.298193 | -0.162113 | 1.777812 | 5.193511 | 4.189214 | 28.167482 | 0.344776 | 0.5110 | 0.5250 | 0.2451 | nan | nan |
| 2459983 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.301358 | 4.365523 | 0.197267 | 1.787136 | 4.493608 | 5.070493 | 37.131119 | 2.892642 | 0.5223 | 0.5432 | 0.2305 | nan | nan |
| 2459982 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 10.834419 | 5.762845 | 0.413566 | 1.802529 | 4.230565 | 2.893287 | 5.351783 | 0.051260 | 0.5993 | 0.6019 | 0.2146 | nan | nan |
| 2459981 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.708019 | 3.916366 | 0.259485 | 1.731707 | 4.642077 | 3.953771 | 52.442228 | 2.046214 | 0.5001 | 0.5093 | 0.2536 | nan | nan |
| 2459980 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 9.123804 | 4.070102 | 0.098677 | 1.613844 | 5.342565 | 4.766744 | 13.645665 | 0.856699 | 0.5553 | 0.5627 | 0.2076 | nan | nan |
| 2459979 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 11.426451 | 4.067262 | 0.025435 | 1.594152 | 3.991882 | 4.208104 | 52.654837 | 1.391457 | 0.4888 | 0.5028 | 0.2508 | nan | nan |
| 2459978 | dish_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 22.596591 | 4.216067 | 12.206548 | -0.549849 | 9.518006 | 3.266843 | 8.558656 | 3.369752 | 0.0354 | 0.5095 | 0.3899 | nan | nan |
| 2459977 | dish_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 23.107919 | 4.219961 | 11.901385 | -0.551722 | 9.390862 | 4.421009 | 7.467624 | 1.624448 | 0.0421 | 0.4642 | 0.3452 | nan | nan |
| 2459976 | dish_maintenance | 100.00% | 100.00% | 0.00% | 0.00% | - | - | 20.621278 | 4.148416 | 11.866322 | -1.207762 | 9.543036 | 3.820731 | 5.706306 | 1.414530 | 0.0389 | 0.5146 | 0.3927 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 65.722287 | 4.247230 | 1.881748 | -0.325029 | 0.175193 | 2.365456 | 2.914956 | 65.722287 | 1.784760 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 70.992282 | 4.588700 | 2.030976 | -0.346204 | 0.188049 | 0.979248 | 1.364839 | 70.992282 | 1.908632 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 15.628826 | 2.017569 | 7.750122 | -0.091244 | -0.023234 | 1.495115 | 1.602641 | 1.101181 | 15.628826 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 28.512872 | 8.016692 | 1.758469 | 0.151539 | 0.159264 | 1.822893 | 1.940729 | 28.512872 | 0.176687 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 34.215793 | 5.431539 | 1.598764 | 0.123779 | 0.183021 | 3.761807 | 2.073243 | 34.215793 | 0.650724 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 51.103773 | 6.446513 | 1.687970 | 0.083252 | 0.268664 | 2.458136 | 0.988358 | 51.103773 | 0.443684 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 26.671990 | 7.047061 | 2.092761 | 0.214648 | 0.139180 | 3.354883 | 1.035974 | 26.671990 | 2.095241 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 22.730507 | 8.936275 | 4.519721 | -0.490373 | 1.334129 | 3.503706 | 3.801128 | 22.730507 | 2.077481 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 19.337135 | 10.959423 | 4.608005 | 0.038723 | 1.865038 | 4.314456 | 4.049612 | 19.337135 | 2.907614 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 25.743274 | 10.569575 | 5.223066 | 0.189829 | 1.942266 | 3.631252 | 4.405093 | 25.743274 | 3.772217 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 37.729546 | 4.269160 | 9.115566 | 1.974764 | 0.242811 | 3.896251 | 4.436532 | 3.611318 | 37.729546 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 33.154457 | 4.201771 | 9.659005 | 2.026232 | 0.177629 | 3.297131 | 4.606690 | 3.706910 | 33.154457 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 30.269848 | 5.199686 | 11.904476 | 1.883782 | 0.165336 | 3.987447 | 4.761895 | 1.482734 | 30.269848 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 44.607472 | 9.860834 | 4.160895 | 0.142020 | 1.873290 | 3.748032 | 3.355355 | 44.607472 | 0.743372 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 22.913180 | 5.116341 | 11.450322 | 1.923145 | 0.201874 | 4.621762 | 5.689827 | 1.507016 | 22.913180 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 57.248630 | 5.017981 | 11.665547 | 1.809779 | 0.091672 | 3.564587 | 3.906339 | 5.503310 | 57.248630 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 28.167482 | 12.434703 | 4.298193 | -0.162113 | 1.777812 | 5.193511 | 4.189214 | 28.167482 | 0.344776 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 37.131119 | 10.301358 | 4.365523 | 0.197267 | 1.787136 | 4.493608 | 5.070493 | 37.131119 | 2.892642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Shape | 10.834419 | 10.834419 | 5.762845 | 0.413566 | 1.802529 | 4.230565 | 2.893287 | 5.351783 | 0.051260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 52.442228 | 3.916366 | 9.708019 | 1.731707 | 0.259485 | 3.953771 | 4.642077 | 2.046214 | 52.442228 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 13.645665 | 4.070102 | 9.123804 | 1.613844 | 0.098677 | 4.766744 | 5.342565 | 0.856699 | 13.645665 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Temporal Discontinuties | 52.654837 | 11.426451 | 4.067262 | 0.025435 | 1.594152 | 3.991882 | 4.208104 | 52.654837 | 1.391457 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Shape | 22.596591 | 4.216067 | 22.596591 | -0.549849 | 12.206548 | 3.266843 | 9.518006 | 3.369752 | 8.558656 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Shape | 23.107919 | 23.107919 | 4.219961 | 11.901385 | -0.551722 | 9.390862 | 4.421009 | 7.467624 | 1.624448 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | N03 | dish_maintenance | ee Shape | 20.621278 | 4.148416 | 20.621278 | -1.207762 | 11.866322 | 3.820731 | 9.543036 | 1.414530 | 5.706306 |